Interaction between Extreme Temperature Events and Fine Particulate Matter on Cardiometabolic Multimorbidity: Evidence from Four National Cohort Studies

Accumulating evidence linked extreme temperature events (ETEs) and fine particulate matter (PM2.5) to cardiometabolic multimorbidity (CMM); however, it remained unknown if and how ETEs and PM2.5 interact to trigger CMM occurrence. Merging four Chinese national cohorts with 64,140 free-CMM adults, we provided strong evidence among ETEs, PM2.5 exposure, and CMM occurrence. Performing Cox hazards regression models along with additive interaction analyses, we found that the hazards ratio (HRs) of CMM occurrence associated with heatwave and cold spell were 1.006–1.019 and 1.063–1.091, respectively. Each 10 μg/m3 increment of PM2.5 concentration was associated with 17.9% (95% confidence interval: 13.9–22.0%) increased risk of CMM. Similar adverse effects were also found among PM2.5 constituents of nitrate, organic matter, sulfate, ammonium, and black carbon. We observed a synergetic interaction of heatwave and PM2.5 pollution on CMM occurrence with relative excess risk due to the interaction of 0.999 (0.663–1.334). Our study provides novel evidence that both ETEs and PM2.5 exposure were positively associated with CMM occurrence, and the heatwave interacts synergistically with PM2.5 to trigger CMM.


INTRODUCTION
Cardiometabolic multimorbidity (CMM) is simultaneous suffering from multiple cardiometabolic diseases (CMDs), 1,2 typically involving heart disease, diabetes, and stroke.According to the WHO, the leading causes of global mortality for CMD are estimated to be ischemic heart disease (1st), stroke (2nd), and diabetes (9th). 3Importantly, patients with CMM face multiplicative risk of mortality compared with single CMDs. 4,5In China, the prevalence of CMM was reported as 11.6−16.9%, 6,7which poses a substantial burden on both families and societies.Even worse, China's aging population and rising incidence of cardiometabolic disorders might lead to a large increase in CMM patients. 6,8Therefore, identifying modifiable risk factors of CMM can guide public health preparedness.Although several risk factors, such as obesity, smoking, and sedentary behaviors, have been wellestablished, 9,10 specific causes of more CMM patients remain unclear.
Emerging studies suggested a positive association between air pollution, especially fine particulate matter (PM 2.5 ), and single CMDs. 11,12The GBD reports estimated that PM 2.5 pollution contributed significantly to burdens of CMDs death, accounting for 14.07% of ischemic heart disease, 13.33% of diabetes, and 16.94% of stroke, respectively. 13owever, existing evidence for the association between PM 2.5 and CMM has yet to be fully accounted for.To the best of our knowledge, only a few studies have examined this topic. 7,14,15pecifically, the Urban and Rural Elderly Population (UREP) survey observed that per 10 μg/m 3 increment in PM 2.5 concentrations was associated with 2.2%−7.6%higher risk of CMM. 7 Similarly, findings from the UK Biobank also reported a positive association between PM 2.5 exposure and CMDs, as well as the progression to CMM. 14 Additionally, PM 2.5 is a complex mixture comprising various chemical constituents, mainly including nitrate (NO 3 − ), organic matter (OM), sulfate (SO 4 2− ), ammonium (NH 4 + ), and black carbon (BC), etc.These constituents have varying health effects and can indicate the emission sources. 16,17It is therefore crucial to identify the key toxic constituents of total PM 2.5 mass to develop targeted environmental control strategies.However, there is currently a lack of evidence regarding the associations between PM 2.5 constituents and CMM.
Amidst climate change, extreme temperature events (ETEs) like heatwave and cold spell are occurring more frequently, presenting a substantial threat to human health.The Multi-Country Multi-City Collaborative Network study revealed that exposure to heat and cold was linked to an elevated cardiovascular mortality, accounting for 0.22% (95%CI: 0.21−0.23)and 0.91% (0.89−0.92) excess death, respectively. 18Given the previous evidence on single CMDs, 19−21 we hypothesize that heatwave and cold spell might also impose adverse effect on CMM, despite a lack of direct evidence currently.Another critical issue was the joint effect of ETEs and PM 2.5 pollution.Climate change could trigger events such as wildfire and sandstorms, exacerbating PM 2.5 pollution levels. 22Additionally, ETEs, particularly heatwave, could accelerate respiratory rates and affect ventilation, resulting in increased inhalation of pollutants. 23Recent evidence indicated that both heatwave and cold spell interact synergistically with PM 2.5 pollution, resulting in an increased risk of circulatory mortality. 24,25However, whether ETEs, PM 2.5 , and its constituents work together synergistically to heighten the risk of CMM remains unknown.
To address these knowledge gaps, we aimed to assess the independent and interaction effects of heatwave, cold spell, and PM 2.5 exposure to the risk of CMM using current national cohorts in China.Our findings will contribute evidence regarding the impact of climate change and air pollution on human cardiometabolic health.

Study Population.
Participants' information was derived from four large-scale nationally representative surveys, involving China Health and Retirement Longitudinal Study (CHARLS), China Longitudinal Aging Social Survey (CLASS), China Family Panel Studies (CFPS), and Chinese Longitudinal Healthy Longevity Study (CLHLS).These national prospective cohorts covered 262 cities in 30 Chinese provinces.−29 Briefly, the four national surveys were all dynamic cohorts, conducting face-to-face personal interviews with baseline participants and newly enrolled individuals during follow-up visits.CHARLS recruited a total of 25,586 adults over 45 years, with a baseline survey (2011−2012) and three follow-up visits in 2013, 2015, and 2018.CLASS initially enrolled 11,511 participants aged over 60 years in 2014, followed by two subsequent visits in 2016 and 2018.CFPS, following a baseline survey in 2010, cumulatively included 57,945 participants aged above 18 years and followed every two years until 2020.CLHLS commenced in 1998 and conducted seven rounds of follow-up surveys with an interval of 2−3 years over the subsequent 20 years.Ultimately, we included 64,140 participants free of CMDs after excluding those with missing key information (Figure S1).Among these participants,  S2).Specifically, we retrieved the daily average temperature (AT) from each NCEI station.We then employed the inverse distance weighted method to interpolate the national raster data set of daily AT.Accounting for the regional-climate variations, we defined ETEs by intensity and duration, based on a relative threshold approach. 30,31Currently, heat or cold cutoff values were determined as the 2.5th, 5th, 7.5th, 10th, 90th, 92.5th, 95th, and 97.5th percentiles of the daily AT for each city during study periods (2008−2020).ETEs were identified as consecutive days with daily AT higher than heat cutoff or lower than cold cutoff values for 2, 3, or 4 consecutive days (Table S1).We then calculated the ETEs day frequency, 32,33 including heatwave and cold spell day frequency, as the total number of occurred-events days.
PM 2.5 concentration along with 5 major chemical constituents, was derived from the Tracking Air Pollution in China data set, 34,35 with a 10 km spatial resolution at a monthly level (2010−2020).Briefly, PM 2.5 concentrations were estimated by a two-stage machine learning model that combined multiplesource information from ground observations, satellite AOD, operational CMAQ, and other ancillary data. 34Concentrations of constituents (NO 3 − , OM, SO , NH 4 + , and BC) were obtained from operational CMAQ simulations using the above PM 2.5 concentrations as the overall constraint. 35This data set had high accuracy with a cross-validation coefficient of determination of 0.64−0.83 in China.All pollutant concentrations were calculated as the city-level based on participants' residence address with a one-year average before the occurrence of the outcome or the end of the study (eMethods 2 and 4).

CMM Definition.
Participants were identified as CMM if they experienced at least two concurrent conditions among heart disease, stroke, and diabetes. 36The above physiciandiagnosed disease was ascertained by self-reported health condition in the following questions.Heart disease status was determined by the question: "Have you ever been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems by a doctor?".Stroke status was determined by the question: "Have you ever been diagnosed with stroke by a doctor?".Diabetes status was determined by the question: "Have you ever been diagnosed with diabetes or high blood sugar?".A detailed explanation was supplied in the eMethods 3.

Covariates.
Covariates were thoughtfully chosen based on existing evidence. 7,14,36Prior-selected continuous covariates included age (years) and body mass index (BMI, kg/m 2 ).Binary covariates involved gender (male or female), smoking or drinking status (never, smoker or drinker), marriage status (married or others), physical activity (yes or no), self-reported hypertension status (yes or no), residence type (rural area or urban community), and address region (northern or southern China, divided by the Qinling-Huaihe Environmental Science & Technology line).Multiple categorical covariates included education status (illiterate, elementary school, middle school, or above).To determine the final set of covariates, we performed a directed acyclic graph (DAG) analysis (eMethod 5).

Statistical Analyses.
We used a semiparametric model with Cox proportional survival hazards regression to examine the risk estimates of ETEs and PM 2.5 pollution on CMM.Initially, we developed a crude model, including ETEs, PM 2.5 , or single constituent, and the outcome.Subsequently, we further adjusted for DAG-selected covariates in the adjusted model.The weighted Schoenfeld residual test and variables' variance inflation factors were successively conducted to examine proportional hazards assumption and potential multicollinearity, respectively.And we did not detect any violation.We performed the Akaike Information Criterion (AIC) to assess model fitness for the multidefined ETEs and determine optimal definitions.And those definitions with minimal AIC values were selected for further analyses.Stratified analyses were also conducted by several modifiers, involving age, gender, BMI status, marriage, education, residence type, regions, smoking, and hypertension.The Ztest was performed to determine the difference between subgroups.Besides, we performed the weighted quantile sum (WQS) regression model with logistic conjunction to examine the joint association of five highly correlated components with CMM (eMethod 6). 37,38Calculated risk estimates were exhibited with hazard ratios (HRs) and 95% confidence interval (CI) related to a specific increment for occurred-ETEs days or PM 2.5 concentrations.
Additionally, we evaluated the interaction effects of ETEs and PM 2.5 exposure on CMM (eMethod 6).We transformed PM 2.5 pollution and its components into binary variables according to the 2021 interim target 1 from the WHO air quality guideline (annual average PM 2.5 : 35 μg/m 3 ) or the median concentrations.Similarly, we converted HW11 and CS10 into binary variables based on their occurrence within the previous year.We then generated a categorical dummy variable with four levels, including nonheatwave and low-level PM 2.5 (X 00 ), non-HW and high-level PM 2.5 (X 01 ), HW and low-level PM 2.5 (X 10 ), and heatwave and high-level PM 2.5 (X 11 ).Similar dummy-variable classifications were also conducted for cold spell and PM 2.5 components.The dummy variable was further included in the Cox regression model, with the X 00 serving as reference.Afterward, we calculated relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and the synergy index (S) for the additive interaction analysis, which was more informative into public health actions.While the corresponding 95%CI were calculated using the delta method. 39.6.Sensitivity Analyses.First, we recalculated the timevarying exposure with 2-year time scale.Second, we eliminated those participants who had a follow-up duration of less than three years to assess the impact of follow-up time.Third, we omitted outcome events that occurred within the initial two years to minimize the risk of reverse causation.Fourth, we further adjusted for other confounders, including drinking status, physical exercise, sleep status, hypertension, and ambient ozone pollution upon the main model.Fifth, we also conducted a nested case-control study based on the current merge data set.Participants with CMM were included in the case group, while the control group was selected using the propensity score matching method with a matching ratio of 1:3 and a caliper value of 0.02 based on the selected factors, involving age, gender, BMI status (underweight, normal, overweight, and obesity), marital, education, and smoking status.We then performed a conditional logistic regression model to determine the association of PM 2.5 and ETEs

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exposure with CMM.All the statistical analyses were operated in R 4.3.1, with statistical significance defined as a two-sided pvalue less than 0.05.

RESULTS
We recruited a total of 64,140 participants with an average age of 51.89 years (SD = 20.58) in 258 Chinese cities between 2008 and 2020 (Figure 1A).The gender distribution was nearly equal, with 50.3% male participants.Approximately 53.2% of the participants were urbanites, and 47.5% were from northern China (Table 1).More detailed characteristics for each separate cohort can be found in Table S2.During the follow-up period of 0.4 million person-years, 1229 new-onset cases of CMM were finally identified, yielding an incidence rate of 3.07 per 1000 person-years.We observed significant differences between CMM (1.9%) and non-CMM (98.1%) groups concerning age, gender, BMI, marital status, education level, residence type, region, hypertension, smoking, and sleep.PM 2.5 concentrations varied between 6.49 and 106.47 μg/m 3 throughout the study duration, with higher levels in northern China (Figure 1B).The northwest region, particularly the Tarim Basin, experienced high levels of heatwave and cold spell (Figure 1).The lower-middle reaches of the Yangtze River were also affected by heatwave exposure (Figure 1C).Specifically, the annual PM 2.5 , HW11, and CS10 exposure for the participants were 40.65 (16.90) μg/m 3 , 5.55 (5.99), and 7.36 (6.29) days, respectively (Table 1).
We observed near-linear and positive associations between PM 2.5 with its constituents and CMM occurrence (Figure 2).In the linear analyses, elevated risks of CMM were associated with PM 2.5 and its constituents, with estimated HRs of 1.179 for PM 2.  2).A mixture analysis also revealed a positive association between the combined exposure to 5 constituents and CMM, with an HR of 1.160 (1.127−1.195).The weights of BC (62.4%) and SO 42− (33.7%) predominated in the mixed exposure (Figure S5), indicating the major contributions to CMM.Further stratification analysis showed that marital status, residence, and region type were the modifiers for the above associations (Tables 3 and S4).Specifically, the participants without cohabitants (HR = 1.296, , sulfate; NH 4 + , ammonium; OM, organic matter; BC, black carbon; HW11, heatwave frequency for the definition of daily average temperature equal to or higher than 97.5th percentile for at least 3 consecutive days; CS10, cold spell frequency for the definition of daily average temperature lower than 2.5th percentile with at least 2 consecutive days.b Z-test p-value < 0.05.c 38440 missing data.d 7368 missing data.e 1479 missing data.f 11986 missing data.

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Figure 3 presented the associations of ETEs with CMM under different definitions.We found that both heatwave and cold spell were positively associated with CMM occurrence after fully adjusting for covariates.Interestingly, the risk estimates of cold spell appeared to be stronger than heatwave exposure.The estimated HR values ranged from 1.063 to 1.091 for cold spell, with the largest HR of 1.091 (1.080−1.102)observed in the CS10.For the heatwave, HW11 showed the largest HRs of 1.019 (1.010−1.028)and minimum AIC value for CMM incidents.Stratification analysis summarized that the associations between heatwave and increased CMM risk were stronger among nonsmokers, while cold spell contributed to greater risks among the urbanites (Table 3).Besides, higher risk estimates for the above association were recognized among participants without cohabitants, with HRs of 1.036 (1.023− 1.049) for HW11 exposure and 1.111 (1.096−1.126)for CS10 exposure, respectively (Table 3).Additionally, the normalweight and the younger adults (<45 years) suffered greater cold-related risks of CMM.
Series of sensitivity analyses showed our findings' robustness.First, we examined the associations of CMM incidents with PM 2.5 constituents and ETEs on a 2-year time scale.We also observed significantly positive associations (Table S6).We then excluded the participants with a follow-up of less than 3 years or those who developed CMM within 2 years, respectively.And the results were generally consistent with the main analysis (Tables S7 and S8).We further adjusted for drinking status, exercise habits, sleep status, and ozone concentrations in the main model, and the association remained significant (Table S9).Furthermore, we developed

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a nested case-control study including 1229 new CMM cases and 3687 free-CMM participants as control.After adjusting several DAG-selected covariates, we observed positive associations of CMM with PM 2.5 , heatwave, and cold spell (Table S10).All the above analyses indicated our result's robustness.

DISCUSSION
To the best of our knowledge, this is the first large-scale cohort study exploring the independent and interaction effects of exposure to ETEs and PM 2.5 with constituents on CMM incidence worldwide.Our findings indicate that increased levels of PM 2.5 pollution, as well as heatwave and cold spell, are environmental risk factors associated with the occurrence of CMM.These results remain consistent across 5 kinds of PM 2.5 constituents, with BC and SO 4 2− playing a particularly significant role.Furthermore, we found synergistically detrimental effects resulting from the coexposure to PM 2.5 and heatwave.
Previous studies conducted in China and the UK have examined the association between PM 2.5 pollution and CMM morbidity. 7,14,15The UREP survey, a cross-sectional study involving 222,179 Chinese adults, indicated that a 10 μg/m 3 increase in PM 2.5 concentrations is related to a 2.2−7.6% higher risk of CMM. 7 Another two cohort studies, enrolling Abbreviations: PM 2.5 , fine particulate matter; CMM, cardiometabolic multimorbidity; HR, hazard ratio; 95%CI, 95% confidence intervals; HW11, heat wave frequency for the definition of daily average temperature equal to or higher than 97.5th percentile for at least 3 consecutive days; CS10, cold spell frequency for the definition of daily average temperature lower than 2.5th percentile with at least 2 consecutive days; BMI, body mass index.b Note: Z-test p-value < 0.05, suggesting that the differences between subgroups are statistically significant.Each stratification controlled for all factors (age, gender, BMI, marital status, education level, smoking status, residence, and region type) except the stratification factor itself.Environmental Science & Technology about 0.4 million UK Biobank participants free of CMDs, also reported positive association between PM 2.5 pollution and the progression trajectory of CMM. 14,15Our results were consistent with the above findings.Discrepancies in estimated risk values may be attributed to variations in participant demographics, outcome definitions, PM 2.5 concentrations, and chemical constituents. 16,40Furthermore, we observed significant associations between 5 major PM 2.5 constituents (NO 3 − , OM, SO 4 2− , NH 4 + , and BC) and higher risks of CMM.To our knowledge, there was no existing direct evidence on PM 2.5 constituents and CMM.−42 The CFPS study revealed the positive associations for the 4 constituents (NO 3 − , SO 4 2− , NH 4 + , and BC) exposure and cardiovascular disease, indicated by calculated HRs of 1.294 to 1.721. 40Evidence from the Jinchang cohort reported that long-term exposure to NO 3 − , NH 4 + , OM, and BC were significantly associated with diabetes. 41Particularly, BC emerged as the primary contributor in CMM development.BC in PM 2.5 primarily stems from the incomplete combustion of fossil fuels and biomass. 43Our results suggest that controlling the use of fossil fuels and developing cleaner fuels may be helpful for preventing CMM.
Another main finding was the positive association between ETEs and CMM occurrence.−46 For instance, a prior study in 31 Chinese capital cities reported that cold spell (CS02) was associated with an increased risk for IHD (RR = 1.67, 1.45−1.89),stroke (1.52, 1.35−1.69),and diabetes (1.66, 1.43−1.89). 45Similar associations were also observed with heatwave (HW05). 46owever, findings from the U.S. reported that there were null associations between extreme heat exposure (99th percentile) and cardiovascular disease or diabetes. 44The heterogeneous results observed in these studies may be attributed to variations in participant characteristics, lifestyle habits, regional climate patterns, definitions of ETEs, and study designs. 24,47However, the above evidence primarily focuses on the acute effect of ETEs, which might be insufficient for understanding the development of chronic diseases such as CMM.Consequently, our study focused on chronic effects by calculating the days of heatwave or cold spell over the past year. 48ith regard to the underlying mechanisms of exposure to PM 2.5 and ETEs on CMM, there was a lack of clear explanations yet.Several generally accepted views were that PM 2.5 pollution could trigger systematic oxidative stress and inflammation, induce intestinal microbial dysbiosis, accelerate atherosclerosis, and disrupt cardiac autonomic function, 49−51 wherein various PM 2.5 constituents exert important roles.BC could induce excess reactive oxygen species and inflammatory markers, as well as alter DNA methylation, thereby accelerating cardiometabolic impairment. 52,53Available biomechanisms for the heatwave involved triggering autonomic neuropathy, accelerating cellular electrolyte disorders, or interfering with glucose tolerance. 46,54The possible mechanistic impact of the cold spell may be due to an increased plasma fibrinogen and coagulation factor VII, potentially damaging vascular endothelium and triggering thrombosis. 45,55Additionally, the cold may also activate sympathetic nervous system and renin-angiotensin system, leading to higher heart rate and increased peripheral vascular resistance. 56Nonetheless, the exact mechanism is worth elucidating in future studies.
Additionally, our findings provide novel evidence suggesting a synergistic effect of air pollution and ETEs on human health.Several epidemiological studies have reported similar interaction effects on mortality or hospital visits. 24,25,57However, the possible synergistic effects of PM 2.5 pollution and ETEs exposure on CMM are not assessed yet.Currently, we evaluated the additive interactions of ETEs and PM 2.5 Abbreviations: PM 2.5 , fine particulate matter; CMM, cardiometabolic multimorbidity; RERI, relative excess risk due to interaction; AP, attributable proportion due to interaction; S, synergy index; HW11, heat wave frequency for the definition of daily average temperature equal to or higher than 97.5th percentile for at least 3 consecutive days; CS10, cold spell frequency for the definition of daily average temperature lower than 2.5th percentile with at least 2 consecutive days; NO

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pollution on CMM development.A significantly synergistic effect was observed with coexposure to heatwave (HW11) and heavier PM 2.5 pollution (>35 μg/m 3 ).In line with our findings, a muti-center study across 24 countries reported that heatrelated cardiovascular mortality increased by 2.07 (2.04−2.10),4.29 (4.26−4.32),and 7.33 (7.29−7.37)under low, median, and high PM 2.5 levels, respectively, suggesting a potential interaction effect. 58Another finding from the CLHLS cohort also reported such a synergistic adverse effect of heatwave and PM 2.5 pollution on hypertension incidence. 59Several existing biomechanisms could potentially explain our findings.A plausible view indicated that high temperatures could increase the uptake of PM 2.5 by triggering an elevated blood flow rate, skin permeability, and respiratory rate. 23Besides, previous evidence showed heatwave and PM 2.5 pollution share common biological pathways involving accelerated oxidative stress injury and systemic inflammation, 23,60,61 which may provoke synergetic health effects.However, further studies are needed to elucidate the specific mechanisms for the current synergetic interaction.
Our subgroup analyses revealed several modifiers in the association of PM 2.5 pollution and ETEs with CMM morbidity.First, we found that the singles were more susceptible to PM 2.5 and ETEs exposure on CMM.From the sociologic viewpoint, the married commonly have well-established social support systems and also have more accessibility to emotional assistance via the marital relationship, 62,63 which may help to alleviate stresses air pollution and ETEs.Besides, the singles are more likely to coexist with CMM-related unhealthy lifestyles, such as smoking or excessive drinking. 64Second, there were stronger risk estimates for PM 2.5 and cold spell exposure among rural and urban residents, respectively.For the PM 2.5 pollution, two published studies were in line with our findings. 7,65And the plausible reasons might attribute to the higher frequency of solid fuels usage in rural cooking and heating, 65 which was reported to have a synergistic effect with PM 2.5 pollution.In addition, the urban residents typically suffer from the cold faster and longer during a cold spell due to the urban heat island. 66Third, we observed that the risk estimate of PM 2.5 pollution was greater in southern China.We hypothesized that the main reason might be the higher PM 2.5 infiltration and exposure factors in southern than northern China. 67,68Specifically, residents in the southern regions open windows for ventilation more frequently than those in the north, enabling a higher infiltration of ambient PM 2.5 indoors and elevating actual human exposure. 67he strengths of this study mainly involved the perspective, large sample size, and nationally representative cohorts with long-term follow-up visits.Specifically, participants were recruited from 258 Chinese cities, covering more than 6 different climatic zones, which greatly enhanced our findings' generalizability.The large sample size, consisting of 64,140 participants across a wide age range, ensured sufficient statistical power for analyzing joint effects and modification effects.In addition, our study focused on the interaction effects of PM 2.5 pollution and ETEs exposure, which extended our understanding of health impacts of the atmospheric environment.
A number of limitations should also be acknowledged.First, the information on CMM was obtained through self-reported questionnaires during each follow-up survey.This approach may underestimate the actual risk effects as self-reported morbidity tends to be lower than the true incidence.Second, PM 2.5 and its constituents' concentrations were measured at the city level for current risk analysis due to the unavailability of exact residential addresses.Thus, misclassification bias was inevitable for current exposure measurement.However, published studies suggested that such a bias is unlikely to alter the current exposure-response relationship greatly. 69hird, several confounders, the usages of air conditioning or heating systems, were not adjusted in the current associations for a lack of relevant information.Fourth, we excluded those participants with CMDs in the baseline survey.Among these participants, there may be a higher proportion of susceptible people with earlier developed CMM, which may underestimate the risk effects.Fifth, the four included cohorts overlapped somewhat in terms of city coverage and follow-up periods and lacked the weights of populations, which may hamper the representativeness of our findings.Finally, estimates of personal exposure relied on the number of days of ETEs, focusing mainly on the frequency of heatwave or cold spell.A comprehensive indicator that considers both the intensity and frequency of extreme temperature events would be beneficial to develop and implement in future studies.
Generally, our study provided epidemiological evidence that exposure to heatwave, cold spell, and PM 2.5 was independently associated with an increased risk of CMM among Chinese adults.Moreover, we observed a synergistic interaction between heatwave and PM 2.5 and its constituents in triggering CMM occurrence.Under the climate change scenario, our novel findings emphasized the multiple benefits of mitigating air pollution to promote cardiometabolic health.

Figure 1 .
Figure 1.Spatial distribution of participants' residential location, PM 2.5 concentrations, and number of days of heatwave and cold spell during study periods.(A) Residential location; (B) PM 2.5 concentrations; (C) heatwave, defined as 97.5th percentile threshold with a 3 consecutive days duration; and (D) cold spell, defined as 2.5th percentile threshold with 2 consecutive days duration.

c
Others included unmarried, separated, divorced or widowed.

Figure 3 .
Figure 3. Associations between different definitions of ETEs and cardiometabolic multimorbidity occurrence.ETEs, extreme temperature events; HR, hazard ratio; CI, confidence interval; HW, heatwave; CS, cold spell.Notes: bolded type indicates that the AIC value was minimum under this definition and also selected for subsequent analysis.

Figure 4 .
Figure 4. Relative risk with contributions of CMM occurrence from different exposure categories.Higher PM 2.5 means heavier pollution over 35 μg/m 3 , and HW is defined as 97.5th percentile threshold with 3 consecutive days duration.HR, hazard ratio; HW, heatwave.

Table 1 .
Baseline Characters and Environmental Exposure for the Study Participants a a Abbreviations: CMM, cardiometabolic multimorbidity; BMI, body mass index; PM 2.5 , fine particulate matter; NO 3 − , nitrate; SO 4 2−

Table 2 .
Associations between PM 2.5 with Its Constituents and CMM a

Table 4 .
Additive Interaction Effects of Exposure to Extreme Temperature Events and PM 2.5 on CMM a a